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BrainFusion: a Low‐Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.

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Název: BrainFusion: a Low‐Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.
Autoři: Li, Wenhao, Gao, Chenyang, Li, Zhaobo, Diao, Yunheng, Li, Jiaxin, Zhou, Jiayi, Zhou, Jing, Peng, Ying, Chen, Guanchu, Wu, Xuecheng, Wu, Kai
Zdroj: Advanced Science; 8/28/2025, Vol. 12 Issue 32, p1-13, 13p
Témata: BRAIN-computer interfaces, MACHINE learning, ELECTROENCEPHALOGRAPHY, VISUAL programming (Computer science), PSYCHOPHYSIOLOGY, NEAR infrared spectroscopy, SOFTWARE frameworks
Abstrakt: This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain–computer interface (BCI) and brain–body interaction research. While ​electroencephalography (EEG)​​‐based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real‐world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross‐modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within‐subject EEG–functional near‐infrared spectroscopy (fNIRS)​​ motor imagery classification using ensemble modeling and 80.2% accuracy in EEG–electrocardiography (ECG)​​ sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG)​, and ECG, BrainFusion provides a low‐code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world. [ABSTRACT FROM AUTHOR]
Copyright of Advanced Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: BrainFusion: a Low‐Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Wenhao%22">Li, Wenhao</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Chenyang%22">Gao, Chenyang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhaobo%22">Li, Zhaobo</searchLink><br /><searchLink fieldCode="AR" term="%22Diao%2C+Yunheng%22">Diao, Yunheng</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Jiaxin%22">Li, Jiaxin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Jiayi%22">Zhou, Jiayi</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Jing%22">Zhou, Jing</searchLink><br /><searchLink fieldCode="AR" term="%22Peng%2C+Ying%22">Peng, Ying</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Guanchu%22">Chen, Guanchu</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Xuecheng%22">Wu, Xuecheng</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Kai%22">Wu, Kai</searchLink>
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  Data: Advanced Science; 8/28/2025, Vol. 12 Issue 32, p1-13, 13p
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  Data: <searchLink fieldCode="DE" term="%22BRAIN-computer+interfaces%22">BRAIN-computer interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTROENCEPHALOGRAPHY%22">ELECTROENCEPHALOGRAPHY</searchLink><br /><searchLink fieldCode="DE" term="%22VISUAL+programming+%28Computer+science%29%22">VISUAL programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22PSYCHOPHYSIOLOGY%22">PSYCHOPHYSIOLOGY</searchLink><br /><searchLink fieldCode="DE" term="%22NEAR+infrared+spectroscopy%22">NEAR infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22SOFTWARE+frameworks%22">SOFTWARE frameworks</searchLink>
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  Data: This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain–computer interface (BCI) and brain–body interaction research. While ​electroencephalography (EEG)​​‐based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real‐world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross‐modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within‐subject EEG–functional near‐infrared spectroscopy (fNIRS)​​ motor imagery classification using ensemble modeling and 80.2% accuracy in EEG–electrocardiography (ECG)​​ sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG)​, and ECG, BrainFusion provides a low‐code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Advanced Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/advs.202417408
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